1. Field of the Invention
The present invention relates to a method an apparatus for automatically detecting breast tumors and lesions in images. More specifically, the present invention relates to a method and apparatus for automatically segmenting, classifying, and detecting breast lesions in ultrasound, digital and analog mammogram, and magnetic resonance imaging (MRI) images.
2. Related Art
Breast cancer is the most frequently diagnosed malignancy and the second leading cause of mortality in women. In the last decade, ultrasound imaging, along with digital mammography, has become the standard for breast cancer diagnosis. Mammography is the most effective method for early detection of breast cancer, and periodic screening of asymptomatic women reduces the mortality rate. Typically, the first step in breast cancer detection is a screening mammogram, which comprises a low-dose x-ray examination on asymptomatic women. This can be followed by a diagnostic mammogram, which is an x-ray examination done to evaluate a breast complaint or to investigate an abnormality found during a physical examination or screening mammogram. Breast ultrasound is sometimes used to evaluate breast abnormalities that are found during screening mammography, diagnostic mammography, or a physical exam. If a suspicious object is found in the ultrasound image, a surgical biopsy or core needle biopsy is then recommended.
Most ultrasound and digital mammograms are manually interpreted by radiologists. However, manual interpretation is often inaccurate, and can fail to detect the presence of breast tumors and lesions. For example, between 10-30% of women who have breast cancer and undergo mammography have negative mammograms. In about two-thirds of these cases, the radiologist failed to detect retrospectively evident cancer. Such misses have been attributed to the subtle nature of the visual findings, poor image quality, or fatigue and/or oversight by the radiologist.
Several algorithms have been developed which claim to automatically classify breast lesions in ultrasound images. However, such algorithms rely on manual delineation of the tumor boundaries, and do not automatically delineate such boundaries. Further, automatically detecting tumors and extracting lesion boundaries in ultrasound images and digital mammograms is difficult due to the specular nature and the variance in shapes and appearances of sonographic lesions, as well as shadowing artifacts and tumor-like structures in the image, such as glandular tissue, coopers ligaments, and sub-cutaneous fat. Such obstacles make it difficult to automatically determine the lesion area using conventional image processing and computer vision techniques alone.
Many of the aforementioned algorithms rely on a priori shape information of the organ or structure of interest in order to effectuate segmentation. For example, a priori shape information has been used to segment ventricular structures in echocardiograms. However, such algorithms are not suitable for detecting breast lesions, due to variances of lesion shapes and the fact that lesion margins are often poorly defined. Region-based methods have been developed (e.g., fuzzy connectedness) which use homogeneity statistics coupled with low-level image features such as intensity, texture, histograms, and gradient to assign pixels to objects. In such methods, if two pixels are similar in value and connected to each other in some sense, they are assigned to the same object. These approaches, however, do not consider any shape information. As a result, such methods cannot deal with shadowing artifacts, which are common in ultrasound images.
Some researchers have proposed hybrid segmentation techniques to detect breast lesions. These approaches seek to exploit the local neighborhood information of region-based techniques, and the shape and higher-level information of boundary-based techniques. However, without manual intervention, these hybrid techniques cannot automatically distinguish other structures in the sonogram, such as sub-cutaneous fat, coopers ligaments and glandular tissue, from the true lesion.
In recent years, automated ultrasonic lesion segmentation schemes have been proposed, including techniques that uses a combination of the maximum a posteriori (MAP) and Markov Random Field (MRF) methods to estimate ultrasound field distortions and label image regions based on the corrected intensity statistics. However, the imaging model breaks down in the presence of shadowing artifacts. Other approaches attempt to automatically extract and classify ultrasonic breast lesions using fuzzy reasoning. All the pixels in the image are initially classified as normal, tumor, or boundary using a LOG filter. Subsequently, three types of images are generated corresponding to the grade of the pixel. The extracted tumor region is then classified as malignant or benign. Such systems do not consider the problem of speckle noise, shadowing artifacts, or tumor-like structures such as glandular and fatty tissue in the image.
Accordingly, what would be desirable, but has not yet been provided, is a method and apparatus for automatically segmenting and detecting breast lesions and tumors in images, including ultrasound, MRI, and digital and analog mammogram images, without requiring human intervention.